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LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations

JING Lida1,2, ZHANG Ji-Feng1,2   

  1. 1. Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;
    2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-12-15 Revised:2021-01-21 Published:2022-06-20
  • Supported by:
    This paper has been partially supported by the Major Program of National Natural Science Foundation of China under Grant Nos. 61690210, 61690212, the National Natural Science Foundation of China under Grant No. 61333003, and also by the Science Center Program of the National Natural Science Foundation of China under Grant No. 62188101.

JING Lida, ZHANG Ji-Feng. LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations[J]. Journal of Systems Science and Complexity, 2022, 35(3): 748-765.

This paper is concerned with the parameter estimation of deterministic autoregressive moving average (DARMA) systems with quantization data. The estimation algorithms adopted here are the least squares (LS) and the forgetting factor LS, and the signal quantizer is of uniform, that is, with uniform quantization error. The authors analyse the properties of the LS and the forgetting factor LS, and establish the boundedness of the estimation errors and a relationship of the estimation errors with the size of quantization error, which implies that the smaller the quantization error is, the smaller the estimation error is. A numerical example is given to demonstrate theorems.
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